Commit 024be491 authored by Roland Denis's avatar Roland Denis
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FIXING CAPS IN TITLE AND MARKDOWN STYLE

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......@@ -25,16 +25,18 @@ The applicant will be responsible for carrying out the WRF simulations required
His (Her) tasks include the regular writing and editing of technical reports for all the part-ners of the project.
The candidate should hold a doctoral degree and require the following skills/experience:
• strong background in atmospheric/earth/climate sciences. Knowledge on renewable energy (solar, wind) and / or the climate of the Southwest Indian Ocean is an ad-vantage.
• recent and relevant high-quality publications in international journals
• experience in global/regional climate modelling (e.g., WRF)
• experience in Linux/UNIX environment
• competency in programming (e.g. Python, R, MATLAB), and the ability to cope with large amounts of data (observations / simulations)
• advanced communication skills in oral and written English.
* strong background in atmospheric/earth/climate sciences. Knowledge on renewable energy (solar, wind) and / or the climate of the Southwest Indian Ocean is an ad-vantage.
* recent and relevant high-quality publications in international journals
* experience in global/regional climate modelling (e.g., WRF)
* experience in Linux/UNIX environment
* competency in programming (e.g. Python, R, MATLAB), and the ability to cope with large amounts of data (observations / simulations)
* advanced communication skills in oral and written English.
A first experience in the implementation and monitoring of projects of this type would be appreciated.
Rigorous and methodical, the applicant will:
• provide the required technical solutions
• work in autonomy and be highly adaptable to change
• have good teamwork and communications skills
\ No newline at end of file
* provide the required technical solutions
* work in autonomy and be highly adaptable to change
* have good teamwork and communications skills
Title: TURBULENT FLOW CONTROL BY MACHINE LEARNING: IMPOSITION OF SYMMETRY AND INVARIANCE CONDITIONS
Title: Turbulent Flow Control by Machine Learning: Imposition of Symmetry and Invariance Conditions
Date: 2020-03-20 09:43
Slug: job_102a6e77ea83c6036ae02e7724abe0d1
Category: job
......@@ -14,26 +14,27 @@ Job_Employer: CNRS
Expiration_Date: 2020-04-30
Attachment: job_102a6e77ea83c6036ae02e7724abe0d1_attachment.pdf
Department: Fluids, Thermal and Combustion Sciences
Research team: Incompressible Turbulence and Control
PhD advisor: Laurent CORDIER (DR CNRS – Pprime, Poitiers)
Co-PhD advisor: Ronan FABLET (PR IMT Atlantique – LabSTICC, Brest)
Co-supervisor: Lionel MATHELIN (CR CNRS – LIMSI, Orsay)
Contact for information: Laurent.Cordier@univ-poitiers.fr
3-year contract: 1715 € raw monthly salary. Funding guaranteed for 3 years.
**Department:** Fluids, Thermal and Combustion Sciences
**Research team:** Incompressible Turbulence and Control
**PhD advisor:** Laurent CORDIER (DR CNRS – Pprime, Poitiers)
**Co-PhD advisor:** Ronan FABLET (PR IMT Atlantique – LabSTICC, Brest)
**Co-supervisor:** Lionel MATHELIN (CR CNRS – LIMSI, Orsay)
**Contact for information:** Laurent.Cordier@univ-poitiers.fr
**3-year contract:** 1715 € raw monthly salary. Funding guaranteed for 3 years.
Key-words: flow control, closed-loop, Machine Learning, imposition of symmetry and invariance conditions, Deep Neural Networks, Recurrent Neural Networks, Reinforcement Learning
**Key-words:** flow control, closed-loop, Machine Learning, imposition of symmetry and invariance conditions, Deep Neural Networks, Recurrent Neural Networks, Reinforcement Learning
Framework.
**Framework.**
In recent years, continuous progress has been made on the performance of both civilian and military aircraft and helicopters, particularly in terms of flight envelope, radiated noise, maneuverability, vibration, etc. However, further improvements can be achieved by using closed-loop fluid flow control around the machine. This strategy consists of using measurements from sensors placed on the system, to adapt, if possible in real time, the control command to impose. From a control point of view, the main interest of closed loop is to improve the robustness of the control law. In practice, the development of closed-loop control strategies is largely complicated by the highly non-linear and multi-scale nature of the turbulent flows encountered in the targeted configurations.
In order to develop efficient control strategies, various issues must be addressed. In the classical approach followed in flow control, it is indeed necessary:
1. to model the flow dynamics;
2. to estimate the state of the system from scattered and/or indirect measurements;
3. to place optimally actuators (used to introduce control) and sensors (used to reconstruct the state);
4. to determine optimally a control law.
Objectives.
**Objectives.**
This thesis will contribute to the development of realistic closed-loop control strategies for unsteady turbulent flows. Applications include the drag reduction around profiles (by attaching the boundary layer or delaying its separation), the reduction of radiated noise, the flow vectorization to improve the maneuverability or to remove some of the moving air spoilers, the decrease of vibrations induced by dynamic stall, etc.
Work program, methodologies and means.
......@@ -45,5 +46,5 @@ Finally, we will couple previously developed neural models to a Deep Reinforceme
The funding is guaranteed for 3 years in the framework of the 80\|Prime interdisciplinary project opened by the CNRS. This topic is at the heart of the CNRS Research Group "Flow Control Separations", whose Director is Laurent Cordier (Pprime).
Applicant profile, prerequisites.
**Applicant profile, prerequisites.**
Master in Fluid Mechanics / Applied Mathematics / Machine Learning. Appetite for interdisciplinary approaches and machine learning. Desire to go beyond the borders.
Title: IGE SPECIALISTE en TRAITEMENT de DONNEES « Projet SWIO-Energy »
Title: Ingé spécialiste en traitement de données « Projet SWIO-Energy »
Date: 2020-03-20 11:05
Slug: job_80afa41bf67251d8ec231ff540d5e87a
Category: job
......@@ -54,26 +54,29 @@ Plus spécifiquement, l’ingénieur(e) Traitement de données procède à la va
Ses tâches comprennent la rédaction et l’édition régulières de rapports techniques à desti-nation des partenaires.
De formation scientifique supérieure minimale niveau Bac + 5, il(elle) aura avant tout des compétences dans les domaines suivants :
• instrumentation, traitement du signal / images
• imagerie satellitaire
• analyses statistiques
• traitement / gestion de données, base de données
• communication RS232, TCP/IP…
• environnement Linux
• outils informatiques de base : bureautique, Web
• bonne connaissance de la langue anglaise
* instrumentation, traitement du signal / images
* imagerie satellitaire
* analyses statistiques
* traitement / gestion de données, base de données
* communication RS232, TCP/IP…
* environnement Linux
* outils informatiques de base : bureautique, Web
* bonne connaissance de la langue anglaise
Les connaissances ci-après sont souhaitables :
• énergies renouvelables liées aux gisements solaire et éolien
• métrologie, instrumentation solaire / météorologique
• techniques de traitement des Big Data
• techniques et procédures d’étalonnage
• milieu de la recherche
• fonctionnement d’un établissement universitaire
* énergies renouvelables liées aux gisements solaire et éolien
* métrologie, instrumentation solaire / météorologique
* techniques de traitement des Big Data
* techniques et procédures d’étalonnage
* milieu de la recherche
* fonctionnement d’un établissement universitaire
Rigoureux et méthodique, l’ingénieur(e) Traitement de données aura les capacités de :
• concevoir les solutions techniques nécessaires
• autonomie, adaptation aux situations
• aisance relationnelle et rédactionnelle
Une première expérience dans la mise en œuvre d’opérations de ce type constitue un atout.
\ No newline at end of file
* concevoir les solutions techniques nécessaires
* autonomie, adaptation aux situations
* aisance relationnelle et rédactionnelle
Une première expérience dans la mise en œuvre d’opérations de ce type constitue un atout.
Title: CLOSED-LOOP FLOW CONTROL BY PLASMA DISCHARGE AND MACHINE LEARNING
Title: Closed-Loop Flow Control by Plasma Discharge and Machine Learning
Date: 2020-03-20 09:45
Slug: job_bf291de0d14ce799e517a0b126c1dfb0
Category: job
......@@ -14,24 +14,25 @@ Job_Employer: CNRS
Expiration_Date: 2020-04-30
Attachment: job_bf291de0d14ce799e517a0b126c1dfb0_attachment.pdf
Department: Fluids, Thermal and Combustion Sciences
Research teams: Incompressible Turbulence and Control (ITC) / Electro-Fluido-Dynamics (EFD)
PhD advisor: Laurent CORDIER (DR CNRS – Pprime/ITC, Poitiers)
Co-PhD advisor: Philippe TRAORE (MCF HDR Université de Poitiers – Pprime/EFD, Poitiers)
Contact for information: Laurent.Cordier@univ-poitiers.fr
3-year contract: 1715 € raw monthly salary. 50% funding guaranteed.
**Department:** Fluids, Thermal and Combustion Sciences
**Research teams:** Incompressible Turbulence and Control (ITC) / Electro-Fluido-Dynamics (EFD)
**PhD advisor:** Laurent CORDIER (DR CNRS – Pprime/ITC, Poitiers)
**Co-PhD advisor:** Philippe TRAORE (MCF HDR Université de Poitiers – Pprime/EFD, Poitiers)
**Contact for information:** Laurent.Cordier@univ-poitiers.fr
**3-year contract:** 1715 € raw monthly salary. 50% funding guaranteed.
Key-words: flow control, closed-loop, plasma actuators, Machine Learning, Neural Networks, Reinforcement Learning
**Key-words:** flow control, closed-loop, plasma actuators, Machine Learning, Neural Networks, Reinforcement Learning
Framework.
**Framework.**
In recent years, continuous progress has been made on the performance of both civilian and military aircraft and helicopters, particularly in terms of flight envelope, radiated noise, maneuverability, vibration, etc. However, further improvements can be achieved by using closed-loop fluid flow control around the machine. Compared to mechanical blowing or suction actuators more often used in flow control, the advantages of plasma actuators come from their non-intrusive nature, low-energy cost, and particularly short-reaction times. These actuators are generally composed of a system of electrodes installed on one of the walls of the area to be controlled. By applying a sufficient potential difference between these electrodes, a plasma discharge is generated, inducing an ion wind which creates a flow tangential to the wall in order to accelerate the flow, and especially modify the velocity profile in the boundary layer.
Objectives.
**Objectives.**
The efficiency of plasma actuators depends to a large extent on their positions on the wall, as well as on numerous other control hyper-parameters (number of electrodes, distances between them, potential difference or electrical power, shape of the electrical signal, frequency of the discharge, etc.). The objective of the thesis is to determine these parameters in order to optimize a previously established performance function. For this, a numerical optimization tool coupling simulation of complex electrostatic phenomena and closed-loop control will be developed. The work will be organized in two broadly coupled axes: i) development of efficient control strategies by machine learning, ii) improvement of the understanding and physical modeling of the mechanisms at work.
Work program, methodologies and means.
**Work program, methodologies and means.**
We propose to numerically derive closed-loop control strategies of different flows. We will treat the numerical simulation aspects of physical mechanisms (electrodynamics and fluid mechanics) and the development of innovative control strategies (Data Driven approaches based on machine learning methods).
We will study two rather emblematic types of flows:
- The flow behind an obstacle (cylinder, wing profile, backward facing step). This type of strongly separated flow is particularly interesting in cases where the objective of the control is to increase aircraft stealth.
- The mixing layer developing at the interaction of two coaxial jets (see Figure). In this application, the objective is to increase the mixing efficiency between the two jets by exciting the mixing layer with plasma discharges.
......@@ -39,5 +40,5 @@ In the first part of this thesis, we will simulate the plasma discharge by solvi
This subject is supported by a half scholarship awarded by the “Direction Générale de l’Armement”. Additional funding will be requested within the framework of the Labex Interactifs (Pprime). This topic is at the heart of the CNRS Research Group "Flow Control Separations", whose Director is Laurent Cordier (Pprime).
Applicant profile, prerequisites.
**Applicant profile, prerequisites.**
Master in Fluid Mechanics / Applied Mathematics / Machine Learning. Appetite for interdisciplinary approaches and machine learning. Desire to go beyond the borders.
Title: TURBULENT FLOW CONTROL BY MACHINE LEARNING
Title: Turbulent Flow Control by Machine Learning
Date: 2020-03-20 09:40
Slug: job_d6539ea50ec4c493b7cb2dc14b868644
Category: job
......@@ -14,24 +14,24 @@ Job_Employer: CNRS
Expiration_Date: 2020-04-30
Attachment: job_d6539ea50ec4c493b7cb2dc14b868644_attachment.pdf
Department: Fluids, Thermal and Combustion Sciences
Research team: Incompressible Turbulence and Control
Supervisor: Laurent CORDIER
Co-supervisor: Lionel MATHELIN (LIMSI)
Contact for information: Laurent.Cordier@univ-poitiers.fr
3-year contract: 1768 € raw monthly salary. Funding guaranteed for 3 years.
**Department:** Fluids, Thermal and Combustion Sciences
**Research team:** Incompressible Turbulence and Control
**Supervisor:** Laurent CORDIER
**Co-supervisor:** Lionel MATHELIN (LIMSI)
**Contact for information:** Laurent.Cordier@univ-poitiers.fr
**3-year contract:** 1768 € raw monthly salary. Funding guaranteed for 3 years.
Key-words: flow control, closed-loop, Machine Learning, Genetic Programming Control, Reinforcement Learning, Recurrent Neural Network, Deep Reinforcement Learning
**Key-words:** flow control, closed-loop, Machine Learning, Genetic Programming Control, Reinforcement Learning, Recurrent Neural Network, Deep Reinforcement Learning
Framework and objectives.
**Framework and objectives.**
In recent years, continuous progress has been made on the performance of both civilian and military aircraft and helicopters, particularly in terms of flight envelope, radiated noise, maneuverability, vibration, etc. However, further improvements can be achieved by using closed-loop fluid flow control around the machine. This strategy consists of using measurements from sensors placed on the system, to adapt, if possible in real time, the control command to impose. From a control point of view, the main interest of closed-loop is to improve the robustness of the control law. Unfortunately, closed-loop control is currently only usable in a fairly limited range of flow configuration. Indeed, a turbulent flow exhibits both a broad spectrum of spatial scales and a very rich temporal dynamics. High-frequency phenomena (of the order of kHz) therefore require sufficiently fast control, able to adapt to changes in the state of the system. The time required to estimate the state of the system and calculate the command is thus less than the millisecond. This observation explains the difficulties of closed-loop control. As both flow manipulation and open-loop control are commonplace, there is very little example of closed-loop control over sufficiently realistic configurations, especially three-dimensional and turbulent configurations.
This thesis will contribute to the development of realistic closed-loop control strategies for unsteady turbulent flows. Applications include the drag reduction around profiles (by attaching the boundary layer or delaying its separation), the reduction of radiated noise, the flow vectorization to improve the maneuverability or to remove some of the moving air spoilers, the decrease of vibrations induced by dynamic stall, etc.
Work program and means.
**Work program and means.**
We propose to use a pure data-driven approach, rather than physical models, and to exploit newly developed Machine Learning methods. Genetic Programming Control (GPC), Reinforcement Learning (RL), Recurrent Neural Network (RNN), and Deep Reinforcement Learning (DRL) seem particularly well suited. We will focus our efforts on the intrinsic difficulties related to turbulent flow control: large-scale system, unknown and time-varying delays between actions and effects on the objective function, statistical non-stationarity, low observability, real-time constraint, etc. Our strategies will be developed and tested on model dynamical systems (Lorenz, Ginzburg-Landau) to facilitate developments and, subsequently, on a turbulent flow configuration, the wake of three staggered cylinders (Fluidic Pinball) individually controlled by unsteady rotation (see Figure). To do this, we will rely on our expertise in control theory, large-scale approximation methods, statistical learning, etc. and our first successes with Machine Learning strategies (Guéniat et al., 2016, Pivot et al., 2017, Mathelin et al., 2017, Bucci et al., 2019).
The funding is guaranteed for 3 years. This subject is part of the ASTRID project FLOWCON (2018-2020) coordinated by Lionel Mathelin (LIMSI). This topic is also at the heart of the CNRS Research Group "Flow Control Separations", whose Director is Laurent Cordier (Pprime). For the submission step, the FLOWCON project was supported by Dassault Aviation via a letter of support.
Applicant profile, prerequisites.
**Applicant profile, prerequisites.**
Master in Fluid Mechanics / Applied Mathematics / Machine Learning. Appetite for interdisciplinary approaches and machine learning. Desire to go beyond the borders
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